Design and Implementation of an Ultralow-Energy FFT ASIC for Processing ECG in Cardiac Pacemakers

Safwat Mostafa Noor, Eugene John, Manoj Panday

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


In embedded biomedical applications, spectrum analysis algorithms such as fast Fourier transform (FFT) are crucial for pattern detection and have been the focus of continued research. In deeply embedded systems such as cardiac pacemakers, FFT-based signal processing is typically computed by application-specific integrated circuit (ASIC) to achieve low-power operation. This brief proposes a data-driven design approach for an FFT ASIC solution, which exploits the limited range of data encountered by these embedded systems. The optimizations proposed in this brief use the simple concept of hashing and lookup table to effectively reduce the number of arithmetic operations required to perform the FFT of an electrocardiogram (ECG) signal. By reducing the dynamic power consumption and overall energy footprint of FFT computation, the proposed design aims to achieve longer battery life for a cardiac pacemaker. The design is synthesized using a 90-nm standard cell library, and gate level switching activity is simulated to obtain accurate power consumption results. The proposed optimizations achieved a low energy consumption of 27.72 nJ per FFT, which is 14.22% lower than a standard 128-point radix-2 FFT when tested with actual ECG data collected from PhysioNet.

Original languageEnglish (US)
Article number8576661
Pages (from-to)983-987
Number of pages5
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Issue number4
StatePublished - Apr 2019


  • Block floating point (BFP)
  • cardiac pacemaker
  • electrocardiogram (ECG)
  • fast Fourier transform (FFT)
  • hash
  • lookup table (LUT)
  • low-power application specific integrated circuit (ASIC)

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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